Using artificial intelligence to improve bladder cancer surgery
Intraoperative integration of artificial intelligence during cystoscopic surgery
This study is looking to make bladder cancer surgeries better by using smart computer technology to help doctors find tumors more accurately, which could lead to fewer cases of cancer coming back for patients.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Stanford University NIH-funded |
| Lab location | 1 site (Stanford, United States) |
| Project ID | NIH-11000260 on NIH RePORTER |
What this research studies
This research aims to enhance the detection of bladder tumors during cystoscopic surgery by integrating artificial intelligence (AI) technology. By employing deep learning algorithms, the study seeks to improve the accuracy of tumor identification and the quality of surgical resection. This could lead to better outcomes for patients by reducing the recurrence of bladder cancer. The research will involve developing an AI framework that augments traditional cystoscopy methods with advanced detection capabilities.
Who could benefit from this research
Good fit: Ideal candidates for this research are patients diagnosed with bladder cancer who are undergoing cystoscopic procedures.
Not a fit: Patients with non-cancerous bladder conditions or those not requiring cystoscopic intervention may not benefit from this research.
Why it matters
Potential benefit: If successful, this research could significantly reduce bladder cancer recurrence rates and improve surgical outcomes for patients.
How similar studies have performed: Previous research has shown promising results with AI integration in surgical procedures, indicating potential for success in this novel application.
Where this research is happening
Stanford, United States
- Stanford University — Stanford, United States (Active)
Researchers
- Principal investigator: Liao, Joseph C — Stanford University
- Study coordinator: Liao, Joseph C
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page below.